Epigenome-wide DNA Methylation Association Study of CHIP Provides Insight into Perturbed Gene Regulation

With age, hematopoietic stem cells can acquire somatic mutations in leukemogenic genes that confer a proliferative advantage in a phenomenon termed “clonal hematopoiesis of indeterminate potential” (CHIP). How these mutations confer a proliferative advantage and result in increased risk for numerous age-related diseases remains poorly understood. We conducted a multiracial meta-analysis of epigenome-wide association studies (EWAS) of CHIP and its subtypes in four cohorts (N=8196) to elucidate the molecular mechanisms underlying CHIP and illuminate how these changes influence cardiovascular disease risk. The EWAS findings were functionally validated using human hematopoietic stem cell (HSC) models of CHIP. A total of 9615 CpGs were associated with any CHIP, 5990 with DNMT3A CHIP, 5633 with TET2 CHIP, and 6078 with ASXL1 CHIP (P <1×10−7). CpGs associated with CHIP subtypes overlapped moderately, and the genome-wide DNA methylation directions of effect were opposite for TET2 and DNMT3A CHIP, consistent with their opposing effects on global DNA methylation. There was high directional concordance between the CpGs shared from the meta-EWAS and human edited CHIP HSCs. Expression quantitative trait methylation analysis further identified transcriptomic changes associated with CHIP-associated CpGs. Causal inference analyses revealed 261 CHIP-associated CpGs associated with cardiovascular traits and all-cause mortality (FDR adjusted p-value <0.05). Taken together, our study sheds light on the epigenetic changes impacted by CHIP and their associations with age-related disease outcomes. The novel genes and pathways linked to the epigenetic features of CHIP may serve as therapeutic targets for preventing or treating CHIP-mediated diseases.


Introduction
A hallmark of aging is the accumulation of somatic mutations in dividing cells.The vast majority of these mutations do not affect cell tness.In rare circumstances, however, a mutation can arise that confers a selective tness advantage, culminating in its expansion relative to other cells.In the hematopoietic system, this process is termed clonal hematopoiesis (CH).Individuals with CH are at increased risk for the development of hematologic malignancies. 1][8] In a whole genome sequencing (WGS) study from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program that included ~ 100,000 individuals across 51 separate studies, large CHIP clones were found to be uncommon (< 1%) in individuals younger than 40 years of age and increased to 12% in those aged 70-89 and 20% in those aged 90 years and older. 6This age-dependent pattern was consistent across CHIP driver genes 6 and has been observed in other studies. 3,7,8 Dmethylation (DNAm), the addition of a methyl group to a cytosine followed by a guanosine (CpG) in DNA, is an epigenetic modi cation that re ects age and environmental exposures.The gene products of the three most frequently mutated CHIP driver genes, DNMT3A, TET2, and ASXL1, are epigenetic regulators 6 .DNMT3A (DNA-methyltransferase 3A) is a methyltransferase that catalyzes the transfer of methyl groups to CpG sites and catalyzes de novo DNA methylation.9 Conversely, TET2 (ten-eleven translocation-2) is a DNA demethylase that catalyzes the conversion of 5-methylcytosine to 5hydroxymethylcytosine, one of the steps leading to eventual demethylation of CpG sites.10 ASXL1 (ASXL transcriptional regulator 1) is involved in histone modi cation.11 Its function in CHIP remains relatively unknown.12 CHIP has been shown to be associated with global DNAm changes, particularly for the DNMT3A and TET2 CHIP driver gene mutations.13 A previous epigenome-wide association study (EWAS) of CHIP in 582 Cardiovascular Health Study (CHS) participants, with replication in 2655 Atherosclerosis Risk in Communities (ARIC) participants, revealed several thousand CpG sites associated with CHIP and its two major CHIP driver genes, DNMT3A and TET2.13 DNMT3A and TET2 CHIP were also found to have directionally opposing DNAm signatures: DNMT3A CHIP mutations were associated with hypomethylation of CpGs, whereas TET2 CHIP was associated with hypermethylation of CpGs, consistent with the canonical regulatory functions of DNMT3A and TET2 elucidated in murine and human model systems.[14][15][16] Despite the wealth of information from the previous EWAS of CHIP 13 , several limitations and knowledge gaps remain.These include the need to use larger sample sizes to enable analyses of less prevalent CHIP driver gene mutations such as ASXL1, explore downstream functions and pathways in uenced by mRNA expression for any CHIP and CHIP subtypes, and identify underlying molecular mechanisms linking CHIP to CVD.
To address these knowledge gaps, we conducted a multiracial meta-analysis of separate EWAS of CHIP in four independent cohort studies (N = 8196; 462 with any CHIP, 261 DNMT3A, 84 TET2, and 21 with ASXL1 CHIP) along with analysis of the associations of CHIP-related CpGs with downstream gene expression.We expanded upon the previous EWAS of CHIP study 13 by adding two cohorts -the Framingham Heart Study (FHS) and the African-American Jackson Heart Study (JHS) -in addition to the ARIC and CHS cohorts.The EWAS ndings were functionally validated using human hematopoietic stem cell (HSC) models of CHIP.Expression quantitative trait methylation (eQTM) analysis identi ed gene expression changes associated with CHIP-associated CpGs.Causal inference analysis using two-sample Mendelian randomization (MR) was performed to gain insight into the molecular mechanisms linking CHIP to CVD.A owchart of the study design is shown in Fig. 1.

Clinical Characteristics of Study Participants
The baseline characteristics of FHS, JHS, CHS, and ARIC participants included in this investigation are presented in Table 1.The mean age at the time of blood draw for whole-genome sequencing (WGS) was 57, 56, and 58 for FHS, JHS, and ARIC, respectively.Participants from CHS were considerably older, with a mean age of 74 years.All four cohorts had more women than men (54-63% women).Overall, CHIP mutations with a variant allele frequency (VAF) ≥ 2% were present in 5% (166/3295) of participants in FHS, 4% (68/1664) in JHS, 5% (142/2655) in ARIC, and 15% (86/582) in CHS.Consistent with previous reports 6 , the three most frequently mutated CHIP driver genes across all cohorts were DNMT3A, TET2, and ASXL1.Eighty percent of individuals with CHIP demonstrated expanded CHIP clones with VAF > 10%.

Epigenome-wide Association Analysis
Race-speci c analysis Race was classi ed based on self-report.In the race-strati ed analysis, we identi ed 2843 CpGs associated with any CHIP, 758 with DNMT3A, 4735 with TET2 CHIP in White participants and 5498 with any CHIP, 5065 with DNMT3A, and 290 with TET2 CHIP in Black participants at Bonferroni-corrected P < 1 (Supplementary Tables 1-6).1290, 675, and 254 CHIP-associated CpG sites were shared between White and Black participants at the Bonferroni-corrected threshold, with concordant directions of effect for any CHIP, DNMT3A, and TET2 CHIP, respectively.
A sensitivity analysis was performed by excluding CHIP cases with VAF < 10%.The results are similar to the multiracial meta-EWAS of any CHIP and are provided in Supplementary Fig. 4 and Supplementary Tables 11-13.Approximately 78% of CpGs (7460/9615) in the meta-EWAS of any CHIP were re-identi ed in the sensitivity analysis, while 312 CpGs were newly identi ed. ) Engineered Human Hematopoietic Stem Cell Models of CHIP Validate EWAS Results We sought to experimentally validate our multiracial meta-EWAS methylation ndings with an in vitro model of CHIP.CHIP was modeled by introducing loss-of-function mutations in DNMT3A, TET2, and ASXL1 in mobilized peripheral blood CD34 + hematopoietic cells, using CRISPR-Cas9. 17After seven days in culture, these cells were ow sorted to isolate a puri ed population of CD34 + CD38 − Lin − cells.

Association of DNA Methylation with Gene Expression and Pathway Analyses
To investigate the functional consequences of CHIP-associated CpGs, we performed gene ontology (GO) and pathway enrichment analysis for genes harboring CHIP-associated CpGs.For any CHIP, DNMT3A CHIP, and ASXL1 CHIP the enriched GO terms related to broad cellular developmental and organismal processes, while for TET2 CHIP the top GO terms related to regulatory and in ammatory processes (Supplementary Tables 14-17).
To understand how differentially methylated CpGs in association with CHIP might alter cellular function, we identi ed gene expression changes associated with CHIP-linked CpGs.We analyzed the associations of CHIP-associated CpGs with changes in cis gene expression (expressed gene [eGene] within 1 Mb of CpG) in 2115 FHS participants whose DNA methylation data and whole-blood RNA-seq data were available.At P < 1 , we identi ed 1658 unique, signi cant cis CpG-transcript pairs for any CHIP, 1059 for DNMT3A CHIP, 1202 for TET2 CHIP, and 1003 for ASXL1 CHIP (Supplementary Tables 18-21 provide the full expression quantitative trait methylation (eQTM) results). 19Across all CHIP cases as well as for CHIP subtypes, the majority of the expressed genes (eGenes) associated with a CHIP-associated CpG were enriched in pathways related to various immune functions and cellular processes at P < 0.05 (Supplementary Tables 22-25).The GO enrichment results, however, were not signi cant after correction for multiple testing.

Association of DNA Methylation with Genetic Variants and Mendelian Randomization Analysis
Cis-methylation quantitative trait loci (cis-mQTL) -genetic loci that are signi cantly associated with CpG methylation levels and located within 1 Mb of their associated CpG -linked 8642 CpGs associated with any CHIP and CHIP subtypes to GWAS Catalog traits/diseases. 20,21 f the cis-mQTL variants, a subset were associated with clonal hematopoiesis traits, particularly myeloid clonal hematopoiesis and the number of clonal hematopoiesis mutations (Supplementary Table 26).
Additionally, enrichment tests of CHIP-associated CpG sites with EWAS catalog traits 22 were performed across 4023 traits using a signi cance threshold of (0.05/4023) (Supplementary

Discussion
We report the results of a multiracial meta-EWAS of CHIP and identi ed thousands of CpG sites across the genome that are signi cantly associated with any CHIP and with DNMT3A, TET2, and ASXL1 CHIP.Of note, the vast majority of the CpGs were transrelative to the CHIP driver gene.This appears to be consistent with the functions of DNMT3A, TET2, and ASXL1 in globally altering DNA methylation levels of CpG sites genome wide, as seen in the EWAS of each of the three CHIP driver genes, where the signi cantly associated CpGs were numerous and located diffusely across the genome.The methylomic signatures of CHIP and CHIP driver genes were experimentally validated with human-engineered CHIP cells.Downstream analyses were conducted to assess whether these alterations in DNA methylation levels may be causally associated with CVD-related outcomes and all-cause mortality.Causal inference analyses using two-sample MR revealed evidence of a possible causal role of CHIP-associated CpGs in various CVD-related traits and all-cause mortality.
For the experimental validation of our meta-EWAS results, any CHIP-associated CpG sites were signi cantly enriched in DNMT3A-engineered cells, which was expected given the overwhelming predominance of DNMT3A CHIP among total CHIP cases reported in our study and several others 3,6,13 .Interestingly, TET2-associated CpG sites were enriched in ASXL1-engineered cells.This nding is consistent with the substantial CpG overlap (~1000 shared CpGs) between TET2 and ASXL1 CHIP from the meta-EWAS and suggests that the epigenetic regulators TET2 and ASXL1 impact several of the same genome regions and may lead to similar downstream consequences.Interestingly, ASXL1-associated CpGs showed no signi cant enrichment in the ASXL1-engineered cells, which may be due to the limited number of ASXL1 CHIP cases in the EWAS.
Two-sample MR analysis identi ed 261 differentially methylated CpG sites that were putatively causally related to one or more CVD traits and/or all-cause mortality.For example, cg11250194 was putatively causally associated with four CVD-related cardiometabolic traits: LDL cholesterol, HDL cholesterol, triglycerides, and fasting glucose.Cg11250194 resides in the FADS2 gene.It is hypomethylated, associated with DNMT3A CHIP ( =-0.022, P=1.6E-13), and replicated in the DNMT3A CHIP-engineered cells.The FADS2 gene encodes the enzyme fatty acid desaturase 2 -the rst rate-limiting enzyme for the biosynthesis of polyunsaturated fatty acids. 25A recent study found that cg11250194 (FADS2) was associated with Alternative Healthy Eating Index and that hypermethylation of this CpG was associated with lower triglyceride levels 26 .Based on our ndings, hypomethylation of this diet-associated CpG may be linked to higher triglyceride levels, putatively increasing the risk for CVD.FADS2 overexpression has also been found to promote clonal formation. 25  13 .Additionally, to our knowledge, we provide the rst EWAS of ASXL1 CHIP and report thousands of novel ASXL1 CHIP-associated CpGs.Through eQTM analysis that identi ed CpG-transcript pairs, the top eGenes in ASXL1 CHIP relate to various immune processes, suggesting that dysregulated immune function, particularly among T cells, may contribute to ASXL1 CHIP-related disease outcomes.This putative role of ASXL1 CHIP in perturbing immune function, speci cally T cell function, has been recently reported using an ASXL1 CHIP conditional knock-in mouse model. 27Notably, several of the ASXL1 CHIPassociated CpGs displayed putatively causal relations to CVD-related traits in MR analysis, including cg11879188 (in ABO).
While there are several strengths of our study, some limitations should be noted.A larger sample size is needed to examine less frequently mutated CHIP driver genes, such as TP53, JAK2, and PPM1D.Additionally, the reported putatively causal associations of CpGs with CVD outcomes and mortality were based on two-sample MR analysis; our ndings warrant validation.Taken together, our study sheds light on the epigenetic changes linked to CHIP and CHIP subtypes and their associations with CVD-related outcomes.The novel genes and pathways linked to the epigenetic features of CHIP may serve as therapeutic targets for CHIP-related diseases.More broadly, our results provide insight into the molecular mechanisms underlying age-related diseases.

Study Cohorts
The Framingham Heart Study (FHS) is a prospective, observational community-based cohort investigating risk factors for CVD.For our discovery sample, DNAm was measured from FHS participants (N=3295) in the Offspring cohort (N=1860; Exam 8; years 2005-2008) 28 and in the Third Generation cohort (N=1435; Exam 2; years 2008-2011). 29CHIP calls were based on whole-genome sequencing of whole blood DNA samples, the majority of which were from FHS Offspring participants at Exam 8 and Gen 3 participants at Exam 2 and temporally concordant with the time of DNAm pro ling.All FHS participants self-identi ed as White at the time of recruitment.
The Jackson Heart Study (JHS) is an observational community-based cohort studying the environmental and genetic factors associated with CVD in African Americans.For our discovery sample, data were collected from 1664 JHS participants. 13DNAm was measured from the majority of JHS participants at visit 1, with a small subset at visit 2. CHIP calls were concurrent with DNAm pro ling and based on whole-genome sequencing of whole blood DNA samples, where the majority were from visit 1 (years 2000-2004) and a subset from visit 2 (years 2005-2008). 13All JHS participants self-identi ed as Black or African American at the time of recruitment.No ancestry outliers were excluded, as inferred based on genetic similarity to reference panels.Similarity to the 1000G AFR reference panel varied by individual (study q1, median, q3 77.9% 84.3% 89.0%) in the methylation and WGS overlap dataset, using estimates from RFMix.
The Cardiovascular Health Study (CHS) is a population-based cohort study of risk factors for CVD in adults aged 65 or older. 2 DNAm was measured from blood samples from participants in years 5 and 9, year 5, or year 9 only.CHIP calls were based on whole-genome sequencing of blood samples, where the majority were taken 3 years before or concurrently with the rst DNAm measurement. 13CHS participants self-reported their race at the time of recruitment.
The Atherosclerosis Risk in Communities (ARIC) is a prospective, multiracial cohort study of risk factor and clinical outcomes of atherosclerosis. 30DNAm was measured from 2655 ARIC participants at visit 2  (1990-1992) or visit 3 (1993-1995).CHIP calls were based on whole exome sequencing of blood samples from visit 2 and visit 3. 13,31 ARIC participants self-identi ed their race at the time of recruitment.

DNA Methylation Pro ling
All the DNA samples were from whole blood.The four cohorts including FHS, JHS, CHS and ARIC, conducted independent laboratory DNAm measurements, quality control (including sample-wise and probe-wide ltering and probe intensity background correction; see additional le 1).DNA methylation was measured in FHS, CHS, and ARIC participants using Illumina In nium Human Methylation-450 Beadchip (450K array) and in JHS participants using the Ilumina EPIC array as previously described 32,33 .

CHIP Calling
For the purposes of this investigation, CHIP was de ned as a candidate driver gene mutation in genes that have been reported to be associated with hematologic malignancy, is present at a variant allele frequency (VAF) of at least 2% in peripheral blood, and is present in the absence of hematologic malignancy. 34CHIP was detected in FHS, JHS, and CHS from WGS blood DNA in the NHLBI Trans-Omics for Precision Medicine (TOPMed) consortium using the Mutect2 software as previously described. 6In ARIC, CHIP calls were based on whole exome sequencing of blood DNA using the same procedure. 6IP is de ned as when an individual harbors at least one pre-speci ed deleterious insertion/deletion or single nucleotide variant in any of the 74 genes linked to myeloid malignancy at a variant allele frequency (VAF) ≥2%. 6TOPMed WGS samples were sequenced to a median depth of 40x, with the sequencing depth ranging from 30x-50x for a speci c region.At this sequencing depth, CHIP can be reliably ascertained with a VAF >10% but CHIP variants with a VAF ≤10% are unable to be robustly captured. 6or a sensitivity analysis, ancestry-strati ed and pooled ancestry meta-EWAS of any CHIP was performed using a more restrictive CHIP clone size of VAF >10% (See Supplementary Figure 4 and Supplementary Tables 11-13).
Cohort-Speci c EWAS The correction of methylation data for technical covariates was cohort speci c.Each cohort performed an independent investigation to select an optimized set of technical covariates (e.g., batch, plate, chip, row, and column), using measured or imputed blood cell type fractions, surrogate variables, and/or principal components.Most cohorts had previous publications using the same dataset for EWAS of different traits, such as EWAS of alcohol drinking and smoking.In this study, those cohorts used the same strategies as they did previously for correcting for technical variables, including batch effects.Linear mixed models were used to test the associations between CHIP status as the predictor variable and DNAm β values as the outcome variable.Information about cohort-speci c models is available in Supplementary File 2.

Meta-analysis
All analyses were contingent on self-reported Black or White race.Previous ancestry inference in these cohort studies 35 suggests high genetic similarity of nearly all self-identi ed White participants to EUR reference panels (including 1000 Genomes).Self-identi ed Black participants have high but variable (average ~80% but may vary based on study and by study participant) genetic similarity to AFR reference panels and have some similarity to EUR reference panels as well.In some cases, extreme ancestry outliers may have been removed during study-speci c QC.However, this has not been thoroughly documented in the data we received from participating studies.Importantly, we do not mean to imply that socially constructed racial identities reported by study participants are synonymous with genetic ancestry.Strati cation by race may, however, capture differential social and environmental exposures within the US, which may impact the epigenome.
The meta-analysis was performed for any CHIP, DNMT3A, and TET2 in White participants from FHS, CHS, and ARIC (n = 4355) and Black participants from JHS, CHS, and ARIC (n = 3841) participants, respectively, using inverse variance-weighted xed-effects models implemented in metagen() function in R packages (https://rdrr.io/cran/meta/man/metagen.html).The summary statistics were used from the previous EWAS of CHIP for the ARIC and CHS cohorts. 13Then, cross-ancestry meta-analysis was performed for White and Black participants (n = 8,196).The meta-analysis was constrained to methylation probes passing ltering criteria in all cohorts.
Supplementary Figure 1 presents QQ plots with genomic control (GC) in ation factor (λ) to illustrate the EWAS results in each cohort and in the meta-analysis.Our observations reveal a prevalence of high in ation factors (λ >1.1) across nearly all studies.Such elevated in ation factors typically signal potential bias in the analysis process.However, it's important to note that in cases where a signi cant portion of CpG sites exhibit differential methylation associated with the outcome (e.g., age and CHIP), this can contribute to the observed high λ values.Moreover, adjusting for additional PCs moderately associated with the outcome may alleviate lambda values, albeit at the expense of reduced power to detect CpGs related to the outcome.To address this, we adopted strategies consistent with those employed by the respective cohorts in previous analyses, focusing on correcting for technical variables and latent factors identi ed in prior studies across multiple outcomes [36][37][38] .Furthermore, prior to meta-analysis, we implemented additional corrections for individual study results exhibiting λ >1.5, ensuring the integrity of our ndings.The statistical signi cance threshold was P <0.05/400,000 ≈ 1 × 10 -7 .A less stringent threshold, the Benjamini-corrected FDR adjusted p-value <0.05, was also used.

Expression Quantitative Trait Methylation (eQTM) Analysis
Association tests of DNAm and gene expression were previously performed in 2115 FHS participants in the Offspring (n=686) and Third Generation (n=1429) cohorts with available whole blood DNA methylation and RNA-seq gene expression data to identify CpG sites at which differential methylation is associated gene expression 19 .Approximately 70,000 signi cant cis CpG-transcript pairs were identi ed at P <1 × 10 -7 .Cis is de ned as CpGs located within 1 Mb of the transcription start site of a mRNA.When calculating the association between CpG sites and gene-level transcripts, linear regression models were used.Residualized gene expression served as the outcome and residualized DNA methylation value as the primary explanatory variable, with adjustment for age, sex, white blood cell count, blood cell fraction, platelet count, ve gene expression PCs, and ten DNA methylation PCs.Through integration of CpGs and gene-level transcripts (mRNAs) from RNA-seq, mRNAs were identi ed that were signi cantly associated with each of the CpGs in cis for any CHIP and the CHIP subtypes. 19,39 hway Enrichment Analysis Enrichment analysis conducted on gene sets comprising genes annotated to CpGs associated with CHIP and major CHIP subtypes, with a signi cance threshold of P <1 × 10 -7 , along with their corresponding eQTM gene sets.The DAVID Bioinformatics online tool was used for the enrichment analysis (https://david.ncifcrf.gov/home.jsp).To improve the focus of this study, only the results of Gene Ontology (GO) terms related to biological process and KEGG pathways were used.7. On day 2 post thaw, mPB CD34 cells were counted and resuspended in Buffer R or GE Buffer.RNP complexes and cells were mixed and electroporated using Neon Pipette (Thermo Scienti c Inc.) with the following settings: 1650V 10ms pulses 3 times.Samples were seeded in expansion media at 400k/mL.

Assessment of Indel Formation
Genomic DNA (gDNA) was isolated and ampli ed with the following conditions: 95˚C for 2 minutes followed by 35 cycles of 95˚C for 45s, 61-62˚C for 1min, 72˚C for 2min a nal extension at 72˚C for 5 minutes using primers towards TET2, ASXL-1, and DNMT3A (Supplementary Figure 8).PCR products were sent to GeneWiz (Azenta Life Sciences) where PCR cleanup and Sanger sequencing was performed.Indel formation was assessed using TIDE (Supplementary Figure 9). 40

FACS of mPB CD34+ Cells
Edited CD34+ cells were sorted at day 7 post CRISPR-Cas9 using a FACSymphony™ S6 Cell Sorter or a BD FACS Aria II to remove differentiated cells.Brie y, CD34+ cells were washed in cell staining buffer (Biolegend) once and stained with antibodies targeting CD34 (Biolegend), CD38 (Biolegend), and Lineage Markers (Biolegend) for 30 minutes at 4˚C in the dark (Supplementary Figure 6).Antibodies from Biolegend are present in Supplementary Figure 10.
EvoC Library Generation and Primary Methylation Analysis DNA was extracted using (Qiagen) from ow cells from 3-5 donors.EvoC libraries were created following manufacturer instructions (Biomodal).Brie y, DNA was sheared using a Covaris LE220 and assessment of input DNA was performed using Bioanalayzer instrument (Agilent) and Qbit (ThermoFisher).Library generation was performed according to the EvoC protocol (Biomodal).

Sequencing of EvoC Libraries
Capture of CpG sites was performed using Twist Human Methylome Panel (Twist Biosciences) and next generation sequencing was completed by using the NovaSeq 6000 (150bp paired-end reads) targeting 160M reads per sample.Biomodal pipeline version 1.1.1 was used to analyze the raw FASTQs with default settings.Brie y adaptor trimming was performed with cutadapt, resolution of R1 and R2 to generate single-end reads with epigenetic information, mapping onto the human genome (GRCh38), and quanti cation of the modi cation state of each CpG site.

Comparisons between EWAS and Biomodal Data
For and for each CpG, read counts from the forward and reverse strand were summed and the mC fraction calculated as the number of reads supporting mC divided by the total number of reads with modi ed or unmodi ed C (excluding reads with A, T or G).Supplementary Files

Figures Figure 1 Overview of Study Design Figure 2 Genome
Figures

Figure 3 Functional
Figure 3

Table 27 )
. For any CHIP, DNMT3A CHIP, TET2 CHIP, and ASXL1 CHIP, the top outcomes re ected CpG sites related to age/aging, alcohol consumption, smoking, and multiple CVD-related traits including body 23,24index (BMI), type II diabetes, and fasting insulin.In support of previous studies reporting ASXL1 CHIP enrichment among smokers23,24, 24% (1462/6078) of ASXL1 CHIP-associated CpGs overlapped with smoking-associated CpGs.Two-sample MR analysis of CHIP-associated CpGs (as exposures) with cis-mQTLs as the instrumental variables in relation to CVD-related traits and mortality (as outcomes) was performed to infer whether differential methylation at CHIP-associated CpGs may causally in uence the outcomes.The signi cantly associated CpGs for any CHIP and for the three CHIP driver genes were tested for causal associations with 22 traits, including all-cause mortality, BMI, LDL cholesterol, hypertension, diabetes, CVD, and smoking.The top 20 CpGs and annotated genes for each trait are reported in Table3(Supplementary Table28displays the full MR results).261 CHIP-associated, differentially methylated CpG sites were identi ed that were putatively causally associated with CVD-related traits and/or all-cause mortality, including eight CpGs for myocardial infarction (MI) (e.g., cg11879188 (ABO), β MR =-0.99,P MR = ), 108 CpGs for blood pressure (e.g., cg20305489 (SEPT9), β MR = 10, P Thus, FADS2 may be an important gene connecting CHIP with diet.Of note, of the 30 CpGs associated with either Mediterranean-style Diet Score or Alternative Healthy Eating Index or both in a 2020 study by Ma et al.26, 17 were CHIP-associated CpGs (~57%) identi ed from our multiracial meta-EWAS of CHIP.The substantial overlap between diet-and CHIPassociated CpGs is consistent with the hypothesis that an unhealthy diet may be associated with CHIP through epigenetic mechanisms./3217) for any CHIP, 89% (2466/2769) for DNMT3A CHIP, and 90% (955/1059) for TET2 CHIP.This is expected, as almost half of the CHIP cases in our meta-EWAS of CHIP are from the previous EWAS of CHIP substantially larger sample size (N=8196, 462 CHIP cases), including all the samples from the previous study.With the larger sample size of the present study, we identi ed 6687, 3524, and 4678 novel CpGs signi cantly associated with any CHIP and with the top two CHIP driver genes DNMT3A and TET2.Of the CpG sites identi ed from the previous EWAS study at P <1, a large proportion overlapped and have concordant directions of effect with CpGs from the multiracial meta-EWAS of CHIP at P <1:91% (2928 The dataset was reduced to the CpGs with signi cant levels of association from each EWAS analysis.For each of these CpGs, methylation validation experiments.S.K and T.H. drafted the manuscript.D.L., L.M.R., K.F., A.B. substantively revised the work.All authors have read and approved the nal version of the manuscript.Competing interestsF.P. is an employee of Biomodal.L.M.R serves as a consultant for the NHLBI TOPMed Administrative Coordinating Center (through Westat).P.N. reports research grants from Allelica, Amgen, Apple, Boston Scienti c, Genentech / Roche, and Novartis, personal fees from Allelica, Apple, AstraZeneca, Blackstone Life Sciences, Creative Education Concepts, CRISPR Therapeutics, Eli Lilly & Co, Foresite Labs, Genentech / Roche, GV, HeartFlow, Magnet Biomedicine, Merck, and Novartis, scienti c advisory board membership of Esperion Therapeutics, Preciseli, TenSixteen Bio, and Tourmaline Bio, scienti c cofounder of TenSixteen Bio, equity in MyOme, Preciseli, and TenSixteen Bio, and spousal employment at Vertex Pharmaceuticals, all unrelated to the present work.Psaty serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson.No other authors have competing interests.The Jimma University Internal Review Board approved the study ensuring all procedures involving human participation adhere to ethical guidelines established by both the Institution and National Research Committee before any research began.40.Brinkman EK, Chen T, Amendola M, van Steensel B. Easy quantitative assessment of genome editing by sequence trace decomposition.Nucleic Acids Res.2014;42(22):e168.Epub 20141009.doi: 10.1093/nar/gku936.PubMed PMID: 25300484; PMCID: PMC4267669.
TablesTables1 to 3are available in the Supplementary Files section